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The paper presents a new INM RAS atmospheric general circulation model, which includes troposphere, stratosphere, mesosphere, and the lower thermosphere, as well as the lower ionospheric regions (INMAIM). Based on the atmospheric part of the INM climatic model INMCM, a new general circulation model was created by adding the middle atmosphere and lower ionosphere description up to 130 km altitudes. A new computational unit for radiative processes calculation was developed for this purpose. For the lower ionosphere a separate plasma chemistry local model was created. The identification of the INMAIM model climate in the mesosphere and lower thermosphere was carried out based on climatological observations. It was shown that model reproduces the general climatic characteristics considerably well. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature, and soil temperature and moisture) under five different configurations (AMIP, AMIP with uniform 4K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3%). Then, the land surface temperatures from the free land experiments are used to perform a set of "AMIP prescribed land" (PL) simulations, which are evaluated against their free land counterparts. The PL simulations agree well with the free land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP prescribed land experiments. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [ABSTRACT FROM AUTHOR]

Previous studies have recognized the societal relevance of climatic extremes on the seasonal time scale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet orwarm seasons). However, these findings have not yet been generalized beyond case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogs to even themost extreme ERA-Interimevents. This approach offers numerous opportunities for systematically analyzing large samples of extreme seasons, and several such analyses are illustrated. We reveal a striking co-occurrence of ElNiño to La Niña transitions and the largest ERA-Interim midlatitude extreme summer events. Moreover, we perform a climate model evaluation with regard to extreme season size and intensitymeasures and estimate how often an extreme winter like the coldNorth American 2013/14 winter is expected anywhere inmidlatitude regions. Furthermore, we present a large set of simulated analogs to this event, whichmakes it possible to studycommonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified. [ABSTRACT FROM AUTHOR]

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require O(105) model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only O(102) model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties. Plain Language Summary: Calibrating climate models with available data and quantifying their uncertainties are essential to make climate predictions accurate and actionable. A primary source of uncertainties in climate models comes from representation of small‐scale processes such as moist convection. Parameters in convection schemes and other parameterizations are usually calibrated by hand, using only a small fraction of data that are available. As a result, the calibration process may miss information about the small‐scale processes in question. This paper presents a proof‐of‐concept, in an idealized setting, of how parameters in climate models can be calibrated using a substantial fraction of the available data, and how uncertainties in the parameters can be quantified. We employ a new algorithm, called calibrate‐emulate‐sample (CES), which makes such calibration and uncertainty quantification feasible for computationally expensive climate models. CES reduces the hundreds of thousands of model runs usually required to quantify uncertainties in computer models to hundreds, thereby achieving about a factor 1,000 speedup. It leads to more robust calibration and uncertainty quantification in the presence of noise arising from chaotic variability of the climate system. We show how uncertainties in climate model parameters can be translated into quantified uncertainties of climate predictions through ensemble integrations. Key Points: We use time averaged climate statistics to calibrate convective parameters and quantify their uncertaintiesWe demonstrate use of the calibrate‐emulate‐sample algorithm to provide efficient calibration and uncertainty quantificationParametric uncertainty in climate predictions is quantified by sampling from the learnt convective parameter distribution [ABSTRACT FROM AUTHOR]

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets. [ABSTRACT FROM AUTHOR]

This paper describes version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). Meteorology for driving GEOS-Chem has been archived from the E2.1 contributions to phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the pre-industrial era and the recent past. In addition, meteorology is available for the near future and end of the century for seven future scenarios ranging from extreme mitigation to extreme warming. Emissions and boundary conditions have been prepared for input to GEOS-Chem that are consistent with the CMIP6 experimental design. The model meteorology, emissions, transport, and chemistry are evaluated in the recent past and found to be largely consistent with GEOS-Chem driven by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) product and with observational constraints. [ABSTRACT FROM AUTHOR]

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a general circulation model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons. [ABSTRACT FROM AUTHOR]

Optimizing the parameters of a land surface process model (LSPM) through data assimilation (DA) can not only improve and perfect the parameterization schemes in the LSPM through the physical mechanism, but also increase its regional adaptability and simulation capability. This has practical importance for improving simulation results and the climate-prediction capability of general circulation models (GCMs) and regional climate models (RCMs). This paper presents a DA-based method for optimizing the parameterization schemes in LSPMs. We optimize the unsaturated-soil water flow (UnSWF) model as an example by developing a soil-moisture assimilation scheme based on the UnSWF model and the extended Kalman filter (EKF) algorithm, and then combining them with the Variable Infiltration Capacity (VIC) model. Using a month as the assimilation window, we used the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to minimize the objective function through simulated and assimilated soil moisture, achieved the best fit with the given objective function measurement, and optimized the parameters of the UnSWF model, including the saturated-soil hydraulic conductivity, moisture content, matrix potential, and the Clapp and Hornberger constant. The optimal values of the model parameters were obtained during the DA period (the year 1986), and then the optimized parameters were used to improve the UnSWF model. Finally, numerical simulation experiments were carried out from 1986 to 1993 to evaluate the simulation capability of the improved model and to explore and realize the DA-based method for optimizing the soil water parameterization scheme in LSPMs. The experimental results indicated that the optimized model parameters improved and perfected the model based on the physical mechanism, and increased its simulation capability; the optimized model parameters had good temporal portability and their adaptability was stronger, achieving the aim of improving the model. Therefore, this method is reasonable and feasible. This paper provides a good reference for DA-based optimization of the parameterization schemes in LSPMs. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the impact of climate change on road infrastructure in South Africa. The approach used built on previous work associated with the UNU-WIDER Development under Climate Change (DUCC) effort, emphasizing understanding of the impact of climate change on roads. This paper illustrates how climate change effects on road infrastructure can be evaluated at a national and provincial level to produce quantitative estimates of climate change from multiple future scenarios. The results of the study indicate that the national-level climate change cost impact in South Africa will vary between US$116.8 million and US$228.7 million annually in the 2050 decade for the median and maximum climate scenarios if a no-adaptation policy action is taken. Conversely, if a proactive adaptation action is taken, these costs can be reduced to US$55.7 million. The savings in costs arising from climate change impacts based on this policy approach is equivalent to building more than 10,000 km of new, secondary paved road by 2050. The analysis focused on the advantage of a proactive policy approach: the upgrade of road infrastructure where climate impacts will exceed current design and maintenance standards. A stressor-response methodology created by the writers is briefly explained, including the application of 54 potential climate futures using general circulation models (GCMs) approved by the Intergovernmental Panel on Climate Change (IPCC). This paper focuses on provincial-level impacts of both a proactive and a reactive approach. Decadal and average annual costs are detailed through 2100. [ABSTRACT FROM AUTHOR]

Dynamics of the stratosphere and ozone layer are among important sources of atmospheric circulation predictability at subseasonal-to-seasonal time scales. The simulation of the stratospheric dynamics with the SL-AV atmospheric general circulation model for the SLAV072L96 seasonal weather prediction configuration is analyzed. The configuration is currently under preoperational testing at the Hydrometcenter of Russia. The model simulates both winter and summer averaged distributions of zonal wind and temperature close to the reanalysis data. The quasi-biennial oscillation of equatorial zonal wind is simulated with a realistic period and amplitude. There is a significant reduction of errors as compared with the previous stratosphere-resolving SL-AV model configuration. It is shown that the stratospheric process simulation enhancement is largely due to the reduction of systematic errors in the simulation of troposphere dynamics. The work on the inclusion of the CHARM photochemical model in the SL-AV model is described. The results of first experiments with the coupled model are given. [ABSTRACT FROM AUTHOR]

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth's climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. [ABSTRACT FROM AUTHOR]

This paper applies the Multivariate Adaptive Constructed Analogs (MACA) statistical downscaling method to 12 general circulation models to produce 21st century projections of fire weather variables over Victoria, Australia, under two emissions scenarios. The statistically downscaled model data accurately replicate the observed distributions of meteorological variables over the contemporary period, but underestimate fire danger extremes in some models. Under each climate scenario, both mean and extreme fire danger are expected to increase. Though there is variation across Victoria, the 12-model average by year for RCP8.5 indicates a 10–20% increase in extreme (99th percentile) Forest Fire Danger Index across the state, with the greatest change projected in the north-west region. At five geographically and climatologically different locations across Victoria, there is a 50–200% increase in the number of days per year exceeding the threshold for the Victorian Very High or higher fire danger rating by the end of the century compared with the start. The high-end warming (RCP8.5) scenario shows increased temperature to be the main driver of heightened fire danger. Changes in temperature, humidity and precipitation during spring and early summer both increase the length of the fire season and may reduce springtime opportunities for prescribed burning. Twelve GCMs are downscaled over Victoria, Australia, to estimate trends in fire weather frequency under two emissions scenarios using the Multivariate Adaptive Constructed Analogs (MACA) method. Increases of ~50–200% in the number of Very High or above fire danger days are likely by 2100, mostly due to higher temperatures. Photo showing smoke from a planned burn in Victoria, Australia, by Nick McCarthy. [ABSTRACT FROM AUTHOR]

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation, which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For 1 month lead time, our simple stochastic model shows similar—but somewhat higher—values of the skill scores than the much more complex deterministic models. [ABSTRACT FROM AUTHOR]

The boreal summer intraseasonal oscillation (BSISO) is among the most pronounced subseasonal variability in the tropics during boreal summer. Compared with its wintertime counterpart, the so-called Madden-Julian oscillation (MJO), the BSISO convection displays more complicated spatiotemporal evolution, characterized by northward propagation over the northern Indian Ocean and western North Pacific as well as eastward propagation along the equator. It exerts a strong effect on a broad range of tropical weather and climate phenomena, such as tropical cyclogenesis, monsoon onset, and active/break cycles, among others. Our fundamental understanding of the BSISO has steadily advanced: so far various aspects of the BSISO have been described, and several theories that aim to elucidate its northward propagation have been proposed. Yet, our skill to simulate the BSISO by general circulation models remains unsatisfactory, though it has been improved. This paper reviews some fundamental aspects of the BSISO from the viewpoint of observation, theory, and modeling. [ABSTRACT FROM AUTHOR]

This paper investigates climate change impacts on the hydrology and water quality of Brazilian semi-arid reservoirs. An integrated approach coupling climate, hydrological and water quality models was proposed. Five general circulation models (GCMs) and two future scenarios (SSP2-4.5 and SSP5-8.5) were used for the projections. The simulations showed that uncertainty in climate forecast significantly impacted hydrological and water quality outputs. Some scenarios revealed a strong decrease in streamflow and storage volume. On the other hand, 40% of multi-GCM projections indicated reservoir eutrophication. The impacts on hydrology and water quality under SSP5-8.5 were more significant than those under SSP2-4.5. The results also indicated that drought events may be prolonged under climate change, principally at the end of the 21st century for SSP5-8.5, and may significantly increase the total phosphorus concentration. This suggests that tropical semi-arid reservoirs could be more vulnerable to eutrophication in scenarios of climate change than those in other regions. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of a Wasserstein generative adversarial network to produce realistic weather situations when trained from the climate of a general circulation model (GCM). To do so, a convolutional neural network architecture is proposed for the generator and trained on a synthetic climate database, computed using a simple three dimensional climate model: PLASIM. The generator transforms a "latent space", defined by a 64-dimensional Gaussian distribution, into spatially defined anomalies on the same output grid as PLASIM. The analysis of the statistics in the leading empirical orthogonal functions shows that the generator is able to reproduce many aspects of the multivariate distribution of the synthetic climate. Moreover, generated states reproduce the leading geostrophic balance present in the atmosphere. The ability to represent the climate state in a compact, dense and potentially nonlinear latent space opens new perspectives in the analysis and handling of the climate. This contribution discusses the exploration of the extremes close to a given state and how to connect two realistic weather situations with this approach. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the level of description of the main physical processes in the Earth's atmosphere in modern models of the general circulation of the Earth's atmosphere and gives a brief overview of the modern models used by the main forecasting centers. The promising directions of the development of models of the general circulation of the Earth's atmosphere are discussed. [ABSTRACT FROM AUTHOR]

The reliability of general circulation models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate, and thus the evaluation of GCM performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCM performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs' abilities to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and for eight subcontinental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the Southern Hemisphere. However, GCMs tend to overestimate the warming rate for domains in the Northern Hemisphere, particularly since the mid-1990s. [ABSTRACT FROM AUTHOR]

The electromagnetic (EM) field variations capable of probing the electrical conductivity of the upper mantle and mantle transition zone have a period range between a few hours and 1 day. At these periods, the dominant source of the EM signals is the ionospheric current system, which has a complex spatial and temporal structure. A concept of global-to-local (G2L) transfer functions can handle spatially complex source by relating global source expansion coefficients with locally measured magnetic (or/and electric) fields. When estimating the G2L transfer functions, the source is commonly expanded into spherical harmonics (SH). In this paper, we explore an alternative parametrization of the source based on a principal component analysis (PCA) of the Fourier transformed output from the physics-based Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM). Specifically, we investigate whether magnetic fields computed in the realistic three-dimensional conductivity model of Earth excited by the PCA-based source agree better with observatory data than those computed in the same model but induced by the SH-based source. Using PCA to capture the source current compared to SH parametrization, we find that agreement with the observatory data is better during magnetically disturbed times and at shorter periods. Vice versa, it is poorer during magnetically quiet times and at longer periods. [ABSTRACT FROM AUTHOR]

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations. [ABSTRACT FROM AUTHOR]

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. While the country-level scale of emissions changes can be estimated in near real time, the more detailed, gridded emissions estimates that are required to run general circulation models (GCMs) of the climate will take longer to collect. In this paper we use recorded and projected country-and-sector activity levels to modify gridded predictions from the MESSAGE-GLOBIOM SSP2-4.5 scenario. We provide updated projections for concentrations of greenhouse gases, emissions fields for aerosols, and precursors and the ozone and optical properties that result from this. The code base to perform similar modifications to other scenarios is also provided. We outline the means by which these results may be used in a model intercomparison project (CovidMIP) to investigate the impact of national lockdown measures on climate, including regional temperature, precipitation, and circulation changes. This includes three strands: an assessment of short-term effects (5-year period) and of longer-term effects (30 years) and an investigation into the separate effects of changes in emissions of greenhouse gases and aerosols. This last strand supports the possible attribution of observed changes in the climate system; hence these simulations will also form part of the Detection and Attribution Model Intercomparison Project (DAMIP). [ABSTRACT FROM AUTHOR]

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climate prediction.net (https://www.climateprediction.net/ , last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events. [ABSTRACT FROM AUTHOR]

Conventional long‐range weather prediction is an initial value problem that uses the current state of the atmosphere to produce ensemble forecasts. Purely stochastic predictions for long‐memory processes are "past value" problems that use historical data to provide conditional forecasts. Teleconnection patterns, defined from cross‐correlations, are important for identifying possible dynamical interactions, but they do not necessarily imply causation. Using the precise notion of Granger causality, we show that for long‐range stochastic temperature forecasts, the cross‐correlations are only relevant at the level of the innovations–not temperatures. This justifies the Stochastic Seasonal to Interannual Prediction System (StocSIPS) that is based on a (long memory) fractional Gaussian noise model. Extended here to the multivariate case (m‐StocSIPS) produces realistic space‐time temperature simulations. Although it has no Granger causality, emergent properties include realistic teleconnection networks and El Niño events and indices. Plain Language Summary: For forecasts less than about 10 days, Numerical Weather Prediction (NWP) and General Circulation Models (GCMs) have been highly successful, yet for longer ranges, their skill is disappointing. This has motivated the development of alternatives that are based on either the strong spatial correlations–teleconnection patterns such El Niño events–or on the long memories whereby the atmospheric state at any moment is strongly influenced by its own past. In particular, a model only using the long memory already rivals GCM monthly and seasonal temperature forecasts: The Stochastic Seasonal to Interannual Prediction System (StocSIPS). In this paper, we answer the question of whether StocSIPS skill can be further improved by using teleconnections. We do this by developing the space‐time m‐StocSIPS model that is optimally forecast by StocSIPS. In m‐StocSIPS, spatial co‐predictors do not improve the skill: There is no causal relation between different locations useful for long‐range predictions. Although m‐StocSIPS has strong spatial correlations and reproduces teleconnection patterns including El Niño events, they cannot be used to improve the long‐memory StocSIPS forecasts. The teleconnections "were already used" to build the history at every location, which is enough to produce the optimal forecast. Key Points: Granger causality shows that spatial correlations in temperature cannot be used to improve on memory‐based predictions of infinitely long seriesScaling‐based long‐range stochastic forecasting is a past value problem not an initial value problemReal‐world statistics and teleconnection patterns can be reproduced with stochastic simulations with a total lack of causal relationships [ABSTRACT FROM AUTHOR]

This paper presents new laboratory-scale numerical simulations of density-driven exchange flows generated across an idealised, submerged sill obstruction under both non-rotating and rotating frames of reference using the Bergen Ocean Model (BOM), a three-dimensional general ocean circulation model. Initial non-rotating BOM simulations are compared directly with previous laboratory data obtained in a large-scale channel facility incorporating an idealised trapezoidal sill. These laboratory experiments demonstrate that the saline intrusion flux across the sill is initially reduced and then eventually fully blocked under increasing net-barotropic flow conditions imposed in the counterflowing upper freshwater layer, with the saline blockage also more evident for reduced sill submergence depths. These parametric dependences are also demonstrated in the equivalent BOM simulations of the non-rotating sill exchange flows, although the numerical model results tend to overpredict both the interfacial velocity and density gradients across the sill (as indicative of suppressed interfacial mixing), as well as the fresh-saline source flux ratio at which full blockage of the saline intrusion occurs. The BOM simulations are then extended to consider rotating sill exchange flow dynamics. In particular, these additional runs demonstrate that Coriolis forces increase the overall blockage of the saline intrusion layer compared to equivalent non-rotating exchange flows, especially when the Rossby number associated with the saline intrusion flow across the sill is considerably less than unity. This effect is largely attributed to the development of Ekman boundary layer dynamics and associated secondary circulations within the bi-directional exchange flows. These are shown to impose strong control on the transverse distribution and extent of the lower saline intrusion flow across the sill and, hence, the parametric conditions under which full saline intrusion blockage is achieved in rotating sill exchange flows. [ABSTRACT FROM AUTHOR]

The westerly jet (WJ) is an important component of atmospheric circulation, which is characterized by prominent seasonal variations in intensity and position. However, the response of the WJ over Asia during the Last Glacial Maximum (LGM) is still not clear. Using general circulation model experiments, the seasonal behaviors of the WJ over central Asia and Japan are analyzed in this paper. The results show that, compared to the present day (PD), the WJ presents a complicated response during the LGM, both in intensity and position. Over central Asia, it becomes weaker in both summer and winter. But over Japan, it is enhanced in summer but becomes diminished in winter. In terms of position, the WJ over central Asia shifts southward in both summer and winter, whereas the WJ over Japan moves southward in summer but does not change obviously relative to PD in winter. Such WJ changes are well explained by meridional temperature gradients in high troposphere, which is closely linked to seasonal thermal anomalies over the Tibetan Plateau (TP). Despite cooler LGM conditions, the anomalous warming center over the TP becomes stronger in summer. Derived from the heat budget equation, the stronger heating center is mainly caused by the weaker adiabatic cooling generated from ascending motion over the area south of the TP. In winter, the cooling over the TP is also strengthened, mostly owing to the subsidence-induced weaker adiabatic heating. Due to the importance of the WJ, the potential role of TP thermal effects should be a focus when explaining the East Asian climate change during the LGM. [ABSTRACT FROM AUTHOR]

Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model. [ABSTRACT FROM AUTHOR]

Assessing the impact of climate change on water demand is a challenging task. This paper proposes a novel methodology that quantifies this impact by establishing a link between water demand and weather based on climate change scenarios, via Coupled General Circulation Models. These models simulate the response of the global climate system to increasing greenhouse gas concentrations by reproducing atmospheric and ocean processes. In order to establish the link between water demand and weather, Random Forest models based on weather variables were used. This methodology was applied to a district metered area in Naples (Italy). Results demonstrate that the total district water demand may increase by 9–10% during the weeks with the highest temperatures. Furthermore, results show that the increase in water demand changes depending on the social characteristics of the users. The water demand of employed users with high education may increase by 13–15% when the highest temperatures occur. These increases can seriously affect the capacity and operation of existing water systems. [ABSTRACT FROM AUTHOR]

This paper concerns the simulation of the water table elevation in shallow unconfined aquifers where infiltration is assumed as the main mechanism of recharge. The main aim is to provide a reliable tool for groundwater management that satisfies water supply managers. Such a tool is a candidate as a physically based alternative to the use of empirical methods or general circulation models. It is based on the use of two widely available sets of data: the water table elevation measurements and soil moisture time series. In fact, the former are usually provided by government agencies on public websites whereas the latter are included in the atmospheric global datasets (reanalysis). It is notable that data from reanalysis are accessible to any citizen and organization around the world on an open-access basis (e.g., Copernicus). In the proposed method, the measured water table elevations are correlated quantitatively with the water fluxes toward the aquifer evaluated using the soil moisture data from ERA5 reanalysis (provided by ECMWF) within a Richards equation-based approach. The analysis is executed using data from the Umbria region (Italy) on both a daily and monthly scale. In fact, these are the time intervals of interest for a proper management of groundwater resources. The proposed relationships include both a logarithmic and linear term and point out the possible different regimes of the shallow aquifers with regard to the recharge due to infiltration. These different mechanisms reflect in the different role played by the water fluxes toward the aquifer in terms of water table elevation changes according to the considered time scale. [ABSTRACT FROM AUTHOR]

This paper describes and analyzes the Reed-Jablonowski (RJ) tropical cyclone (TC) test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). The intermediate complexity test case analyzes the evolution of a weak vortex into a TC in an idealized tropical environment. Simulations from general circulation models (GCMs) that participated in DCMIP2016 are analyzed in this study at 50 km horizontal grid spacing, with 5 of these models also providing simulations at 25 5 km grid spacing for an analysis on the impact of finer grid spacing. Evolution of minimum surface pressure (MSP) and maximum 1 km azimuthally averaged wind speed (MWS), the wind-pressure relationship, radial profiles of wind speed and surface pressure, and wind composites are documented for all participating GCMs at both horizontal grid spacings. While results are generally similar between all models, some GCMs reach significantly higher storm intensities than others, ultimately impacting specific characteristics of their horizontal and vertical structure. TCs simulated at 25 km grid spacings retained these differences, but reach higher intensities and are more compact than their 50 km counterparts. These results indicate dynamical core choice is an essential factor in GCM development, and future work should be conducted to explore how specific differences within the dynamical core affect TC behavior in GCMs. [ABSTRACT FROM AUTHOR]

In Part I of this paper, a generalized modeling framework for representing deep moist convection was presented. The framework, called unified parameterization, effectively unifies the parameterizations in general circulation models (GCMs) and cloud-resolving models (CRMs) and thus is applicable to any horizontal resolution between those typically used in those models. The key parameter in the unification is the fractional convective cloudiness σ, which is the fractional area covered by convective updrafts in the grid cell. The central issue of Part I is to formulate the σ dependence of vertical eddy transports of thermodynamic variables and to determine σ for each realization of grid-scale processes. The present paper completes the formulation through further analysis of the simulated data. The analyzed fields include the vertical structure of the σ dependence of vertical and horizontal eddy transports of moist static energy and horizontal momentum and that of cloud microphysical sources. For the momentum transport, the analysis results clearly show the limits of the traditional approach of parameterization based on an effectively one-dimensional model. For cloud microphysical conversions, it is shown that those taking place primarily inside and outside the updrafts are roughly proportional to σ and 1 − σ, respectively. [ABSTRACT FROM AUTHOR]

This paper examines the uncertainty in the change in the heat content in the ocean component of a general circulation model. We describe the design and implementation of our statistical methodology. Using an ensemble of model runs and an emulator, we produce an estimate of the full probability distribution function (PDF) for the change in upper ocean heat in an Atmosphere/Ocean General Circulation Model, the Community Climate System Model v. 3, across a multi-dimensional input space. We show how the emulator of the GCM's heat content change and hence, the PDF, can be validated and how implausible outcomes from the emulator can be identified when compared to observational estimates of the metric. In addition, the paper describes how the emulator outcomes and related uncertainty information might inform estimates of the same metric from a multi-model Coupled Model Intercomparison Project phase 3 ensemble. We illustrate how to (1) construct an ensemble based on experiment design methods, (2) construct and evaluate an emulator for a particular metric of a complex model, (3) validate the emulator using observational estimates and explore the input space with respect to implausible outcomes and (4) contribute to the understanding of uncertainties within a multi-model ensemble. Finally, we estimate the most likely value for heat content change and its uncertainty for the model, with respect to both observations and the uncertainty in the value for the input parameters. [ABSTRACT FROM AUTHOR]

Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin. [ABSTRACT FROM AUTHOR]

Knowledge of the daytime variability of cloud fraction is pivotal for the accurate determination of the atmosphere's energy balance. Yet, polar orbiting satellites are unable to fully capture daytime variability and individual geostationary satellites only provide measurements for portions of the Earth. In addition, diurnal cloud fraction variations analyzed with General Circulation Models provide results that cannot be reconciled with observations in many situations. Here, we show that the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite presents a unique opportunity to examine changes in daytime global cloud fraction resolved at one‐hour frequency. For the first time, we characterize the global daytime variability of clouds with a single sensor, presenting evidence that the fraction of liquid clouds reaches its maximum over continental areas and its minimum over ocean around noon. We compare our results with previous global and regional studies and examine implications for the Earth's energy budget. Plain Language Summary: Cloud coverage changes during the day over every location of the Earth. While those movements might seem random for a particular day, patterns emerge when cloud fraction is evaluated with sufficient data in local times. The Earth Polychromatic Imaging Camera (EPIC) aboard the DSCOVR satellite observes the Earth from approximately 1.5 million kilometers away, capturing the whole sunlit side of the planet at once. Therefore, each one of EPIC's images contains information about daytime cloud fraction at different local times, with early morning and late afternoon in the left and right edges respectively. As EPIC takes between 10‐22 images per day, we can track the daytime evolution of cloud fraction with a single sensor. In this paper, we aggregate seasonally four years of data to show that there are more liquid clouds over land and less over ocean around noon. On the contrary, ice clouds evolve independently of the underlying surface type, with larger cloud fraction in the morning and afternoon and a minimum at noon. Key Points: Global daytime cycles are analyzed with a single sensor for the first time using DSCOVR/EPICLiquid cloud fraction is maximum over land and minimum over ocean around noonIce cloud fraction is higher in the morning and afternoon than around noon independently of the surface type [ABSTRACT FROM AUTHOR]

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services. [ABSTRACT FROM AUTHOR]

The responses of North American precipitation to the Atlantic warm pool (AWP) are investigated by using observational data and the NCAR Community Earth System Model. We show that the responses are controlled by different physical mechanisms in different seasons. In the warm season, a large AWP reduces the North Atlantic subtropical high, consistent with Gill's physics. The corresponding influence over North America is northerly wind anomalies in the lower atmosphere, which leads to a precipitation suppression in the central United States. However, in the cold season the AWP's impact on North American precipitation is operated via the teleconnection of cold SST anomalies in the tropical Pacific which are induced by the AWP. A large AWP enhances the local Hadley circulation that moves across the equator to the tropical southeastern Pacific in the boreal summer. This inter-hemispheric process strengthens the atmospheric sinking and then increases the South Pacific subtropical high, resulting in the enhancement of the surface easterly trade wind and thus cold SST anomalies in tropical southeastern Pacific. The wind-evaporation-SST and Bjerknes feedbacks further lead tropical central Pacific SST to a La Niña-like pattern, which is consistent with that the significant and negative correlation between ENSO and the AWP is observed after the 1990s when the AWP leads by 6–9 months. The AWP-induced La Niña-like SST anomalies in the tropical central Pacific further prompts a negative phase of the Pacific North American teleconnection, resulting in decreased precipitation over the southern United States; and vice versa. In addition to the AWP-induced central Pacific-type of ENSO events, the paper also shows the influences of the AWP on SSTs in the North Pacific and Indian Oceans. [ABSTRACT FROM AUTHOR]

When solving hydrodynamic equations in spherical or cylindrical geometry using explicit finite-difference schemes, a major difficulty is that the time step is greatly restricted by the clustering of azimuthal cells near the pole due to the Courant–Friedrichs–Lewy condition. This paper adapts the azimuthal averaging–reconstruction (ring average) technique to finite-difference schemes in order to mitigate the time step constraint in spherical and cylindrical coordinates. The finite-difference ring average technique averages physical quantities based on an effective grid and then reconstructs the solution back to the original grid in a piecewise, monotonic way. The algorithm is implemented in a community upper-atmospheric model, the Thermosphere–Ionosphere Electrodynamics General Circulation Model (TIEGCM), with a horizontal resolution up to 0.625∘×0.625∘ in geographic longitude–latitude coordinates, which enables the capability of resolving critical mesoscale structures within the TIEGCM. Numerical experiments have shown that the ring average technique introduces minimal artifacts in the polar region of general circulation model (GCM) solutions, which is a significant improvement compared to commonly used low-pass filtering techniques such as the fast Fourier transform method. Since the finite-difference adaption of the ring average technique is a post-solver type of algorithm, which requires no changes to the original computational grid and numerical algorithms, it has also been implemented in much more complicated models with extended physical–chemical modules such as the Coupled Magnetosphere–Ionosphere–Thermosphere (CMIT) model and the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (WACCM-X). The implementation of ring average techniques in both models enables CMIT and WACCM-X to perform global simulations with a much higher resolution than that used in the community versions. The new technique is not only a significant improvement in space weather modeling capability, but it can also be adapted to more general finite-difference solvers for hyperbolic equations in spherical and polar geometries. Highlights. The ring average technique is adapted to solve the issue of clustered grid cells in polar and spherical coordinates with a finite-difference method. The ring average technique is applied to develop a 0.625∘×0.625∘ high-resolution TIEGCM and more complicated geoscientific models with polar and spherical coordinates as well as finite-difference numerical schemes. The high-resolution TIEGCM shows good capability in resolving mesoscale structures in the ionosphere–thermosphere (I–T) system. [ABSTRACT FROM AUTHOR]

The Arctic regional coupled sea-ice–ocean–atmosphere model (ArcIOAM) has been developed to provide reliable Arctic sea ice prediction on seasonal timescales. The description and implementation of ArcIOAM and its preliminary results for the year of 2012 are presented in this paper. In the ArcIOAM configuration, the Community Coupler 2 (C-Coupler2) is used to couple the Arctic sea-ice–oceanic configuration of the MITgcm (Massachusetts Institute of Technology general circulation model) with the Arctic atmospheric configuration of the Polar WRF (Weather Research and Forecasting) model. A scalability test is performed to investigate the parallelization of the coupled model. As the first step toward reliable Arctic seasonal sea ice prediction, ArcIOAM, implemented with two-way coupling strategy along with one-way coupling strategy, is evaluated with respect to available observational data and reanalysis products for the year of 2012. A stand-alone MITgcm run with prescribed atmospheric forcing is performed for reference. From the comparison, all the experiments simulate reasonable evolution of sea ice and ocean states in the Arctic region over a 1-year simulation period. The two-way coupling has better performance in terms of sea ice extent, concentration, thickness and sea surface temperature (SST), especially in summer. This result indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) observations in the North Atlantic since 1870 reveal a region of enhanced warming off the northeastern coast of North America, and a region of cooling to the south of Greenland. It has been hypothesized that these adjacent SST trends are a result of long‐term changes in the buoyancy‐driven ocean circulation—a slowdown of the Atlantic Meridional Overturning Circulation. The impacts of these historical SST trends on the atmosphere are estimated using idealized atmospheric general circulation model experiments in which the global atmosphere is exposed to modern climatological forcing minus the aforementioned regional SST trends. The local response includes a negative North Atlantic Oscillation tendency and southward shift of the wind forcing for the subtropical gyre. Due to planetary wave propagation, the regional SST trends also induce a northward shift of the intertropical convergence zone over the Indian Ocean. Implications for climate feedbacks and projections are discussed. Plain Language Summary: The ocean surface is warming over most of the planet, with a couple of notable exceptions. One is over the North Atlantic Ocean, just south of Greenland, where the temperature of the ocean surface has actually been cooling by about 1°C since 1870. Nearby, extending off the northeastern coastline of North America is a region where the ocean surface has been warming much faster than the global ocean on average—by about 1.5°C since 1870. This side‐by‐side pair of accelerated warming and cooling trends is likely due to a slowdown of the ocean's overturning circulation, which carries large amounts of heat energy from the tropics toward the poles. This paper reveals the effects of those unusual ocean temperature trends in the North Atlantic on the atmospheric circulation using a computer model of the global atmosphere. The model simulations indicate that these ocean temperature trends are causing shifts in the jet stream, changing the way the winds propel the upper ocean currents, and even have impacts quite far away by moving the tropical rain belt northward in the Indian Ocean. Key Points: The impact of historical sea surface temperature trends in the North Atlantic since 1870 is simulated with a global atmospheric modelAdjacent warming and cooling trends in the North Atlantic Ocean induce a negative NAO‐like response and southward‐shifted gyre forcingPropagation of planetary waves away from the North Atlantic induces a northward‐shifted intertropical convergence zone in the Indian Ocean [ABSTRACT FROM AUTHOR]

A simplified model of the atmospheric boundary layer (ABL) of intermediate complexity between a bulk parameterization and a three-dimensional atmospheric model is developed and integrated to the Nucleus for European Modelling of the Ocean (NEMO) general circulation model. An objective in the derivation of such a simplified model, called ABL1d, is to reach an apt representation in ocean-only numerical simulations of some of the key processes associated with air–sea interactions at the characteristic scales of the oceanic mesoscale. In this paper we describe the formulation of the ABL1d model and the strategy to constrain this model with large-scale atmospheric data available from reanalysis or real-time forecasts. A particular emphasis is on the appropriate choice and calibration of a turbulent closure scheme for the atmospheric boundary layer. This is a key ingredient to properly represent the air–sea interaction processes of interest. We also provide a detailed description of the NEMO-ABL1d coupling infrastructure and its computational efficiency. The resulting simplified model is then tested for several boundary-layer regimes relevant to either ocean–atmosphere or sea-ice–atmosphere coupling. The coupled system is also tested with a realistic 0.25∘ resolution global configuration. The numerical results are evaluated using standard metrics from the literature to quantify the wind–sea-surface-temperature (a.k.a. thermal feedback effect), wind–current (a.k.a. current feedback effect), and ABL–sea-ice couplings. With respect to these metrics, our results show very good agreement with observations and fully coupled ocean–atmosphere models for a computational overhead of about 9 % in terms of elapsed time compared to standard uncoupled simulations. This moderate overhead, largely due to I/O operations, leaves room for further improvement to relax the assumption of horizontal homogeneity behind ABL1d and thus to further improve the realism of the coupling while keeping the flexibility of ocean-only modeling. [ABSTRACT FROM AUTHOR]

Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event > ∼ 51% of extreme events; 2- σ : > ∼ 98 % ). Our primary objective is to contrast the magnitude (∘C) and frequency of occurrence of extreme temperature events between the twentieth and twenty-first century. We project that temperatures will increase substantially in the twenty-first century. Extreme Ta events will experience the largest increases by magnitude, and extreme Ts events will experience the largest increases by proportion. On average, 2-σ extreme Ts events will increase by 3.4 ∘C in 2030–2065 and by 5.3 ∘C in 2065–2100. Increases in extreme Ts events will often exceed + 10 ∘C at 0–20 cm by 2065–2100, and at 0–100 cm will often exceed 5.0 standard deviations above 1980–2015 values. 2-σ extreme Ts events will increase from 0.9 events per decade in 1980–2015 to 23 events in 2030–2065 and 38 events in 2065–2100. By 2065–2100, the majority of months will experience extreme events that co-occur at 0–100 cm, which did not occur in 1980–2015. These projections illustrate the non-analog temperature increases that ecosystems will experience in the twenty-first century as a result of climate change. [ABSTRACT FROM AUTHOR]

We present a neutral diffusion operator appropriate for an ocean model making use of general vertical coordinates. The diffusion scheme uses polynomial reconstructions in the vertical, along with a horizontally local but vertically nonlocal stencil for estimates of tracer fluxes. These fluxes are calculated on a vertical grid that is the superset of model columns in a neutral density space. Using flux‐limiters, the algorithm dissipates tracer extrema locally, and no new extrema are created. A demonstration using a linear equation of state in an idealized configuration shows that the algorithm is perfectly neutral. When using the nonlinear TEOS‐10 equation of state with a constant reference pressure, the algorithm compares nearly exactly to a case discretized onto isopycnal surfaces and using along‐layer diffusion. The algorithm's cost is comparable to that of tracer advection and can be readily implemented into ocean general circulation models. Plain Language Summary: One prominent effect of ocean turbulence is the mixing of quantities such as temperature, salinity, and other tracers carried by the seawater. For turbulence associated with geostrophic motions, this mixing occurs along neutral directions, which are directions where buoyancy does not change. In computer models that do not explicitly represent such geostrophic eddy motions, this type of turbulent mixing is parameterized by diffusion oriented along the neutral directions. A common means to represent neutral diffusion is via rotating the diffusion fluxes to align with neutral directions (which are typically not aligned with the model grid lines). Unfortunately, this rotation can result in spurious numerical artifacts such as the creation of new minimum or maximum values of tracer concentration (referred to as extrema). In this paper, we propose a new algorithm for calculating and applying neutral diffusion in sublayers of a numerical model. We show that this new method does not create extrema, and it provides a physically accurate representation of turbulent diffusive mixing along neutral directions. Key Points: A new discretization of a neutral diffusion operator is detailed, with the new method not based on a rotated tensorThe new algorithm overcomes many numerical deficiencies of previous approachesIdealized test cases show minimal spurious dianeutral diffusion for linear and nonlinear equations of state [ABSTRACT FROM AUTHOR]

This paper presents the results of studies on water resources management strategies, under the influence of climate change, in the Zarrineh River basin, providing some 41% of the total inflow into the Lake Urmia. Climate change simulation over the period 1992–2050 was performed by downscaling of seven global circulation models. The best model was then selected for modelling the hydrological behavior of the basin using a rainfall-runoff model while an allocation model was used to evaluate future supply and demands. A system dynamics model was further utilized to investigate the sustainability of the dynamic behavior of the basin. Five suggested water resources management strategies for two future climate scenarios were investigated. Results indicated that implementation of the strategy of 40% reduction in agricultural water demand would provide higher degrees of sustainability. Based on this, the average annual inflow to Lake Urmia in the years 2021 to 2050 would be 1029.75 MCM, which is equal to 81.02% of the Lake Urmia's environmental requirement from the river's share. [ABSTRACT FROM AUTHOR]

Reconstruction of the atmospheric circulation from the numerical output of general circulation model (GCM) and reanalysis products often suffers from spurious values resulting in imbalances in the mass, energy, and water transport. A correction hence is needed prior to the analysis of energy transport. Most of the previous correction methods only employ a mass budget adjustment since it is the primary source of error. The present study proposes a new method of mass flux correction by constraining the mass, energy, and water budgets that can be applied to meridional energy transport and overturning circulation. The new method seeks a vertical dependent solution, unlike the conventional barotropic correction procedures. The procedure has been tested for one aquaplanet GCM and a reanalysis product. The method successfully corrects the spurious imbalances in the mean meridional energy transport and circulation. Plain Language Summary: Meridional energy transport is an essential climate diagnostic. However, the calculation of energy transport from climate model output and reanalyses often encounters spurious values due to mass, energy, and water budget imbalances. Although climate models are based on fundamental conservation laws, the numerical models often slightly deviate due to various interpolations, approximations, and low resolution. Traditionally, such imbalances are corrected by adjusting the mass flux to satisfy mass conservation, which corrects the spurious values in energy transport to a certain extent. The present paper proposes a new mass flux correction method that satisfies not only the mass conservation but also the energy and water conservation. Unlike the conventional vertically uniform correction, the present method provides a vertically dependent correction. Moreover, the new mass flux correction can also be applied to correct the mean meridional circulation. The result demonstrates that the new method is a remarkable improvement from the conventional methods. Key Points: The present study proposes a new flux correction method for vertically integrated zonal mean energy transport in the atmosphereThe proposed method satisfies the conservation of mass, energy, and water vapor, which was not a characteristic of most previous methodsUnlike the previous methods, the new approach provides a vertical dependent correction [ABSTRACT FROM AUTHOR]

Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]

In the present paper, we study the climatological annual mean Hadley circulation simulated by 47 coupled atmospheric and ocean general circulation models (AOGCMs) of the phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our results show that the climatological annual mean states of the Hadley circulation, i.e., strength, central line, and poleward boundaries of the Hadley cells, have a large inter-model spread. The strength of the Hadley cells, i.e., the maximum value of mean meridional streamfunction (MMS), varies from 7.0 × 1010 to 11.1 × 1010 kg s− 1. The variation range is about half of the multi-model ensemble mean strength of about 8.7 × 1010 kg s− 1. The central line location between the two Hadley cells varies from 3.7° S to 7.8° N, and the poleward boundary latitudes have a variation range of 5.5° in latitude. In contrast, inter-model spreads of the Hadley circulation are much smaller in reanalyses. Using a method of inter-model empirical orthogonal function (EOF) analysis of annual-mean MMS, we show that the first principal component (PC1) is closely correlated with the central line latitudes of the Hadley cells, and that the second principal component (PC2) has a high correlation with the poleward boundaries. Both PC1 and PC2 are significantly correlated with the strength of the Hadley cells. Regressions of sea surface temperatures (SSTs) and precipitation on PC1 and PC2 show that EOF1 is associated with the well-known double intertropical convergence zone (ITCZ) problem in AOGCMs, and that EOF2 is related to the cold tongue bias. The results suggest that the large inter-model spread of the climatological mean Hadley circulation is largely due to the double ITCZ and cold tongue biases, and that the key for improving the simulation performance of the Hadley circulation is to reduce the double ITCZ and cold tongue bias in AOGCMs. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration (NASA) Goddard Earth Observing System global circulation model (GCM) is evaluated through a cascade of simulations with increasing horizontal resolution. This model employs a nonhydrostatic dynamical core and includes a scale‐aware, deep convection parameterization (DPCP). The 40‐day simulations at six resolutions (100 km to 3 km) with unvarying model formulation were produced. At the highest resolution, extreme experiments were carried out: one with no DPCP and one with its scale awareness eliminated. Simulated precipitation, radiative balance, and atmospheric thermodynamic and dynamical variables are well reproduced with respect to both observational and reanalysis data. As model resolution increases, the convective precipitation smoothly transitions from being mostly produced by the convection parameterization to the cloud microphysics parameterization. However, contrary to current thought, these extreme cases argue for maintaining, to some extent, the scale‐aware DPCP even at 3‐km scale, as the run relying solely on explicit grid‐scale production of rainfall performs more poorly at this resolution. Plain Language Summary: Global circulation models (GCMs) are the means we have to predict the weather and assess the future climate on daily, seasonal, and even century‐long timescales. GCMs are computational tools that employ approximate forms of the fundamental equations governing the time evolution of the atmosphere. Given the broad spectrum of scales of the atmospheric phenomenon, computational limitations require parameterizations for processes not explicitly resolved at the model computational grid. The next generation of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS) GCM is being developed as a unified, seamless model to be applied across a broad spectrum of spatial and temporal scales of the atmospheric motions. This paper provides an evaluation of relevant aspects of the GEOS forecasts across a pertinent range of horizontal resolution with an unvarying model formulation. We focus specifically on the parameterization of deep convection and evaluate its influence on model results down to the a few kilometers scale. We arrive at the unexpected conclusion that a kilometer‐scale resolution where convection is mostly explicitly resolved perform worse. Key Points: The Goddard Earth Observing System global circulation model is evaluated across a wide spectrum of horizontal resolutionAcross scales, the convective precipitation smoothly transitions from mostly produced by subgrid‐ to grid‐scale model formulationsContrary to current views, poorer results occur when scale‐aware deep convection parameterization is avoided at grid spacings of a few kilometers [ABSTRACT FROM AUTHOR]

This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E–146.5° E, 14.8° S–27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976–2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December–January–February (DJF) and June–July–August (JJA) over future periods of the early (2011–2040), mid (2041–2070) and late twenty-first century (2071–2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10–20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10–30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections. [ABSTRACT FROM AUTHOR]